AppenDiX ii mAcro-economic scenArios - WUR

AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs

AppenDiX iimAcro-economic scenArios

2 AppenDiX ii mAcro-economic scenArios AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs 3

Author

Martin Banse

LEI (Agricultural Economics Research Institute)

PO Box 29703

2502 LS The Hague

The Netherlands

Website: www.lei.nl

AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs 3


prefAceThe Bio-based Raw Materials Platform (known as PGG), which is part of the Energy

Transition programme in the Netherlands, commissioned the Agricultural

Economics Research Institute (LEI) and the Copernicus Institute of Utrecht

University to study the macro-economic impact of large-scale deployment of

biomass for energy and materials in the Netherlands. Two model approaches were

applied based on a consistent set of scenario assumptions: a bottom-up study

including techno-economic projections of fossil and bio-based conversion

technologies and a top-down study including macro-economic modelling of (global)

trade of biomass and fossil resources. The results of the top-down study (part II)

including macro-economic modelling of (global) trade of biomass and fossil

resources, are presented in this report.


AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs 54 AppenDiX ii mAcro-economic scenArios AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs 5

BAckgrounDUnder the framework of previous studies, e.g. EUruralis and Matisse, 1st-generation

(and in a rudimentary way also 2nd-generation) biomass have already been

implemented in the quantitative tool the general equilibrium model GTAP at the LEI

institute, covering the currently available technologies to use food and feed crops to

produce bio-fuels for the transport sector to substitute for common oil products.

This study focuses on the macro-economic consequences on the Dutch economy of

the large-scale use of biomass resources for transportation fuel, bio-electricity and

chemicals1. For the set-up of the various scenarios for biomass uses, a link has been

developed between the uses of biomass inputs estimated in the bottom-up approach

and the quantitative modelling approach.

Previous studies on increased use of 1st-generation biomass focused on achieving

specific targets and subsequently analysing macro-economic implications (e.g. for

food and land prices). In this study, the combination of bottom-up scenarios (with a

range of selected bio-based value chains) has been ‘enforced’ onto the macro-

economic model, i.e. the estimated quantities of biomass utilisation in various

sectors in the bio-based economy have been translated into blending targets to be

applied in the macro-economic model.

However, there are also clear limitations to this link between the bottom-up and the

top-down approach: one is the lower level of detail of the economic model in

comparison to the bottom-up system calculations. Another element is that the

macro-economic model does take into account key (macro) trends on aspects such as

productivity changes, economic growth and sectoral shifts at large and that, as a

consequence, impacts for specific sectors may appear as relatively minor because

they are ‘overruled’ by such macro trends. The work focuses on delivering results

for bio-based oriented scenarios compared to a baseline to circumvent this problem,

but it should be noted that results obtained at sectoral level are sensitive to the

underlying macro trends.

1 Methodological Approach

The quantitative analysis of this study is based on an extended version of the

LEITAP model, where previous model versions have been applied in the EUruralis

project (Version 2.0) and in the MATISSE project (coordinated by PBL, the

Netherlands Environment Assessment Agency).

LEITAP is a global computable general equilibrium model that covers the entire

economy, including factor markets and is often used in WTO analyses and CAP

analyses. More specifically, LEITAP is a modified version of the global general

equilibrium model GTAP (Global Trade Analysis Project). The model, and its

1 Theuseofbiomassinputsinthefineorbulkchemicalsectorisdependentonthedifferent

technologyassumptioninthescenarios.

AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs 5AnAlysis of the economic impAct of lArge-scAle Deployment of BiomAss resources for energy AnD mAteriAls in the netherlAnDs 5

underlying database, describes production, use and international trade flows of

commodities, services and inputs between regions of the world. Assumptions about

population growth, technological progress, and policy framework are the main

drivers behind the model’s results. Based on such assumptions, the model

determines production, use and trade flows as a result of market clearing on all

commodity and input markets in all countries/regions of the world. Agricultural

policies are treated explicitly (e.g. production quotas, intervention prices, tariff rate

quotas, (de)coupled payments.2

In previous projects the LEITAP model has been extended to include 1st-generation

biomass production. For this extension the approach separates energy from non-

energy intermediate inputs and presents energy inputs in a capital-energy

composite [Burniaux and Truong, 2002]. It extends this methodology by explicitly

depicting the use of cereals, vegetable oils and sugar-beet or sugar-cane as inputs

in the production of biofuels in a multi-level structure in the petroleum activity.

This extension enables researchers to analyse the impact of targeted policies such

as tax exemptions and obligatory blending for the petroleum sector for individual

regions and countries.

In addition to the extensions directly related to modelling biofuels, the extended

version of LEITAP includes some key characteristics of related markets. The

functioning of the land market is particularly crucial. Therefore we included a new

demand structure to reflect that the degree of substitutability of types of land

differs between land types [Huang, et al., 2004] and we included a land supply curve

to include the process of land conversion and land abandonment [Meijl et al., 2006].

Furthermore, in the new LEITAP version, agricultural labour and capital markets

are modelled as segmented from the non-agricultural factor markets.

The LEITAP model has been extended for this project:

for nested input structure, which separates energy from non-energy –

intermediates in the bio-based sectors, i.e. petrol, electricity as well as bulk and

specialty chemicals

for 2 – nd-generation bio-energy crops (‘woody crops’) which are used in the

petroleum sector, in the electricity sector, in the gas sector3 and, depending on

the scenario, also in the chemical sector

for an implementation of policy instruments which allow for a separate –

treatment of mandatory blending targets in the petrol, the electricity and the

fine chemical sectors.

The demand structure of the petroleum industry has been extended to include the

use of woody crops in the similar nest as other bio-based inputs for an ethanol

composite (see Figure 1).

2 AfulldescriptionofLEITAPispresentedintheannexofthisreport.

3 Woodycropsinthegassectorareusedasanintermediateforsyngasproduction.


The model allows for energy and capital substitution in the petroleum and the

electricity sector. Compared to the standard presentation of production technology

the extended LEITAP model aggregates all energy-related inputs for the petrol

sector, such as crude oil, gas, electricity, coal, petrol products, under the nested

structure under the added-value side. At the highest level the energy-related inputs

and the capital inputs are modelled as an aggregated ‘capital-energy’ composite

(Figure 1).

To introduce the demand for bio-energy inputs, the nested CES function has been

adjusted and extended to model the substitution between different categories of oil

(oil from bio-energy and crude-oil), ethanol and petroleum products and in the

added-value nest of the petroleum sector. The non-solid aggregate is modelled the

following way: 1) the non-solid aggregate consists of two sub-aggregates, fuel and

gas. 2) Fuel combines oil seeds, crude oil, petroleum products and ethanol.4

3) Ethanol is made out of sugar-beet/cane, cereals and woody crops.

Figure 1: Capital-energy composite in the Extended LEITAP in the Petroleum Sector

4 Ethanolisnotmodelledasaproductforfinaldemandbutonlyasanaggregatedcompositeinputin

thebio-basedindustries.Asapartofthecompositeintermediateinputethanolwoodycropisalso

an‘indirect’substituteinthebiodieselproduction.

Capital Energy

Non-electric Electricity

Coal Non-solid fuels

Fuel sFuel Gas

Oil-seeds Crude Oil Petroleum Ethanol sEthanol

products

Sugar-beet Cereals Woody crops


This approach is able to present an energy sector where industry’s demand of

intermediates strongly depends on cross-price relation of fossil energy and

biomass-based energy. Therefore, the output prices of the petrol-industry will be

(amongst others) a function of fossil energy and biomass prices. The nested CES

structure implies that crucial variables for the demand for biomass are the relative

price developments of crude oil versus the development of the agricultural prices.

Also important is the initial share of bioenergy inputs in the production of fuel. A

higher share implies a lower elasticity and greater impact on the oil markets.

Finally, the substitution possibilities between crude oil and biomass (represented

by the substitution elasticities sFueland s

Ethanol) are crucial. These represent the

degree of substitutability between crude oil and bio-energy crops. The values of the

elasticity of substitution are taken from Birur et al. [2007], who – based on a

historical simulation of the period 2001-2006 – obtained for sFuel

a value of 3.0 for

the US, 2.75 for the EU, and 1.0 for Brazil.5 These values are applied here and are

kept constant under the LowTech scenarios. Under the HighTech scenarios 2nd-

generation biomass technologies are assumed to be available. Therefore, the

elasticities of substitution between fossil energy and biomass inputs are increased

by 50% for the period 2010-2020, and are set at 100% higher values for the years

2020-2030. As technology progresses over time it might be expected that biomass

and fossil fuels become closer substitutes.

Biomass inputs in the fine chemical industries are mainly used as a substitute for

fossil energy inputs. Therefore, we model the demand for biomass inputs in the fine

chemical industries similar to the approach applied for the petroleum industries.

There are two exceptions: in the chemical sector the demand of ethylene is modelled

via the demand for petroleum products and the demand of hydrogen from biomass

via the demand for gas inputs.

The demand structure of the electricity industry differs from the input demand

structure of the petroleum and fine chemical industries. The model also allows for

energy and capital substitution in the electricity sector. Compared to the standard

presentation of production technology the extended LEITAP model aggregates all

energy-related inputs for the electricity sector, such as crude oil, gas, electricity,

coal, petrol products, under the nested structure under the added-value side.

To introduce the demand for bio-energy inputs the nested CES function in the

electricity sector has been adjusted and extended to model the substitution between

different categories in the composite of non-electric inputs in the following way: 1)

the non-electric aggregate consists of two sub-aggregates, solid and non-solid

inputs, 2) non-solid combines gas, crude oil and petroleum products and 3) the solid

composite is made out of forestry, woody crops and coal, see Figure 2.

5 FortheparametersEthanol

a50%higherelasticityisappliedthanforsFuel.

Foradiscussiononthese

technicalparametersseealsoBanseetal.2008andBiruretal.2008.


In the scenarios calculated for this study we apply the estimated biomass volumes

in the bio-based industries as blending targets which have to be implemented by the

fuel, electricity or fine chemical sectors. Depending on the technology and the ratio

between fossil and biomass input prices, the estimated amount of biomass could

differ from that which would have been applied by the three sectors in a situation

without these blending targets, i.e. it might be simply unprofitable for these sectors

to apply the estimated amount of biomass.

Figure 2: Capital-energy composite in the Extended LEITAP in the Electricity Sector

Implementation of scenarios with an increased use of biomass inputs

If the estimated amount of biomass utilisation is smaller than the optimal amount

based on cost-minimising behaviour of the industries, a subsidy is given to the bio-

based industries to achieve the specified biomass shares. This has also been

implemented for the modelling of the EU Biofuels Directive, which fixes the share of

biofuels in transport fuel. It should be mentioned that the payment of subsidies on

biomass inputs in the bio-based industries are modelled as so-called ‘budget

neutral’ from a government point of view. To achieve this in our model two policies

were implemented: first, the biomass share in transport fuel, the bioenergy share in

electricity and the biomass share in the fine chemicals are made exogenous and

second, a subsidy on biomass inputs is made endogenous to achieve the specified

biofuel share.

In the case of low technologies and moderate fossil energy prices, the subsidies in

the bio-based sectors are necessary to make the biomass inputs competitive with

fossil energy. These policy instruments (input subsidies in the bio-based sectors on

Capital Energy

Non-electric Electric

Non-solid Fuel Solid

Gas Oil Petroleum products Forestry Woody Crops Coal


bioenergy inputs)6 are implemented as ‘budget-neutral’ subsidies that are counter-

financed by an end-user tax on petrol and electricity consumption.

Budget equations are introduced in the model in which end-user tax receipts

provide the income and the input subsidies provide the spending. In the case of a

mandatory blending, the budget surpluses are made exogenous and considered

equal to zero. The end-user tax on petrol and electricity are made endogenous to

generate the necessary budget to finance the subsidy on inputs necessary to fulfil

the mandatory blending. Due to the end-user tax, consumers pay for the mandatory

blending as end-user prices of blended petrol and electricity increase. The higher

prices are the result of the use of more expensive bioenergy inputs relative to fossil

energy inputs in the production of fuel and electricity.

It should, however, be mentioned that the LEITAP model is based on the assumption

that technical changes do not occur in a sudden shift from one technology to

another, i.e. even under the HighTech scenario 2nd- and 1st-generation biomass

inputs will be used in the bio-based industries. Substitution between one input (1st-

generation biomass) with another input (2nd-generation biomass) is modelled as a

continuous function. This approach differs from most analyses based on linear-

programming or technology approaches with thresholds, and sudden and drastic

shifts in different technology options.

GTAP data used

Version 6 of the GTAP data was used for simulation experiments. The GTAP

database contains detailed bilateral trade, transport and protection data

characterising economic linkages among regions, linked together with individual

country input-output databases which account for intersectoral linkages. All

monetary values for the data are in $US millions and the base year for version 6 is

2001. This version of the database divides the world into 88 regions. An additional

interesting feature of version 6 is the distinction of the 25 individual EU Member

States. The database distinguishes 57 sectors in each of the regions; i.e. for each of

the 65 regions there are input-output tables with 57 sectors that depict the

backward and forward linkages amongst activities. The database provides

considerable detail on agriculture, with 14 primary agricultural sectors and seven

agricultural processing sectors (such as dairy, meat products and further

processing sectors).

The social accounting data was aggregated to 37 regions and 13 sectors (see Annex

Tables A1 and A2, respectively). The sectoral aggregation distinguishes agricultural

6 Pleasenotethatinputsubsidiesaregrantedforcereals,oilseeds,woodycropsandsugar-beets/

caneinthepetrolandthefinechemicalsectors.Intheelectricitysectorforestryinputsareas-

sumednottobeeligibleforsubsidies,whilethewoodycropsinputsareeligibleforsubsidies.The

useofforestry,however,istakenintoaccountforthecalculationofthebioenergysharesinthe

electricitysector.


sectors that can be used for producing bioenergy crops (e.g. grains, wheat, oilseeds,

and sugar cane/beet) and that use land, and bio-based industries that demand

biomass (crude oil, petroleum, gas, coal and electricity). The regional aggregation

includes all EU-15 countries (with Belgium and Luxembourg as one region) and all

EU-12 countries (with Baltic regions aggregated to one region, with Malta and

Cyprus included in one region, and Bulgaria and Romania aggregated to one region)

and the most important countries and regions outside EU, from an agricultural

production and demand point of view.

Adjustment of the GTAP 6 database towards biomass production and bio-based

industries

Developments in the biofuel sector are extremely fast, so we updated the GTAP

database to include the latest developments. The calibration of the utilisation of

biomass in LEITAP is based mainly on sources published in F.O. Licht’s World

Ethanol and Biofuel Reports as well as the F.O. Licht Interactive Database for

Ethanol and Biofuels [F.O. Licht, 2007]. Current uses of biomass for liquid biofuel

production at EU Member State level are derived from Eurostat and publications by

the European Commission. For implementing 1st-generation biomass crops the

GTAP database has been adjusted to include the input demand for grain, sugar and

oilseeds in the petroleum industry. Under the adjustment process the total

intermediate use of these three agricultural products at national level has been kept

constant, while the input use in non-petroleum sectors has been adjusted in an

endogenous procedure to reproduce 2005 biofuels shares in the petroleum sector

(corrected for their energy contents).

For the extension of 2nd-generation bio-energy crops the production and

consumption data of the GTAP sector ‘other crops’ has been adjusted by the so-

called ‘splitcom’ program, which allows us to divide the production and

consumption data in GTAP according to defined shares. Here the information of the

production of bioelectricity in the EU Member States and the use of wood and wood

wastes, as published in the European Biomass Statistics 2007 [Kopetz et al., 2007].

In the original database the chemical industry is presented as a single sector. To

show the impact of growing biomass utilisation in the fine chemical industry we

also applied the Splitcom program to separate the fine chemical industries from the

rest of the chemical sector. Due to the limited information about the composition of

the Dutch chemical industry (in value terms), we based our assumption for the

disaggregation of the chemical sector on the quantity shares in the chemical

industries.7

7 Duetothelackofdataweappliedashareof20%forfineand80%forbulkchemicalssimilarto

Wielenetal.[2006].


2 scenArio Description AnD moDel results

2.1 Scenario description

To assess the impact of an increasing use of biomass in the bio-based industries, i.e.

liquid petrol, chemicals, gas and electricity, we applied the estimated biomass

blending shares as presented in Tables 5-7 in Part I of the report describing the

bottom-up scenarios. As outlined in Part I of the report, in addition to the four main

scenarios that include single chemical representatives, an additional scenario

(IntHighTechAC) was created that includes bio-based production of natural gas and

petroleum products in both the specialty and bulk chemical industries. The same

assumptions on GDP and population growth have been applied to all five scenarios

in this study. Apart from those coefficients that define the differences between the

HighTech and the LowTech, as well as the differences between the National and the

International scenarios, all other parameters are kept constant over the scenarios.

The main difference between the HighTech and the LowTech scenarios is the

different degree of the substitutability between biomass and fossil inputs in the

bio-based industries. We assume that under the LowTech scenario production of the

bio-based industries is mainly based on current (1st-generation biomass)

technologies. Therefore, 1st-generation biofuels can be substituted for fossil fuels.

However, especially under the LowTech scenarios, the efficiency of biomass

conversion is assumed to be low, i.e. at current level, which leads to a relative low

elasticity between fossil and biomass energy inputs. The values of the elasticity of

substitution are taken from Birur et al. [2007].

To identify the effect of an enhanced use of biomass inputs we also ran all four

main scenarios without a mandatory blending obligation for biomass use in the bio-

based industries, i.e. petrochemicals, electricity and chemicals. It should be

mentioned that even without a mandatory blending, the use of biomass inputs

changes due to changes in relative prices (biomass crops vs. fossil fuel). Especially

in the HighTech scenarios, it can be assumed that the required subsidies for the

biomass use will strongly decline due to the high technological progress we assume

for these scenarios.

To illustrate the long-term development the results are presented for the initial

period, for 2010, 2020 and 2030.

2.2 Scenario results

Under all scenarios calculated for this study, the Dutch trade balance deteriorates

significantly, see Figure 3. This decline is triggered by a strong increase in GDP and

private income. In all scenarios Dutch GDP is projected to increase by around 60%

between the initial period (2006) and the final projection year, 2030. Imports

increase at a similar rate while overall exports increase by just 12%. As a


consequence of this general macro-economic development, which is not related to

any specific tendency of the ‘bio-based economy’, the Dutch trade balance becomes

increasingly negative.

However, with an enhanced biomass utilisation, the increasing trade deficit is

partly compensated due to a substitution of fossil energy imports by the increasing

use of biomass in the bio-based industries. Under the IntHighTechAC scenario this

effect is most visible. Under the scenarios where the use of biomass is not enforced

(NoBFD), the resulting trade balance is projected to be more negative compared to

the scenarios where biomass use is implemented as mandatory.

It should be mentioned that, even without an enforced use of bio-energy crops

through a mandatory blending, the shares of bio-energy inputs increase in fuel

consumption for transportation purposes and in electricity.

Figure 3: Balance in total trade, in bln €, the Netherlands

11

-90.0

-80.0

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

NatLowTech NatHighTech IntLowTech IntHighTech IntHighTechAC

Initial 2010 2020 2030 2030, NoBFD

Figure 3: Balance in total trade, in bln €, Netherlands

The Dutch balance in trade with biomass crops also declines, see figure 4. Figure 4 presents

the development of the aggregated Dutch trade balance in biomass crops regardless whether

these products are used for food, feed purposes or as inputs in the bio-based industries. Under

the two ‘Int’ scenarios with open trade with all trading partners, Dutch biomass imports

increases and the trade balance becomes more negative, especially in the IntHighTech

scenario in which biomass use is high.

This strong increase is also due to relative high biomass blending shares modelled especially

under the IntHighTech and IntHighTechAC scenarios. The lower trade deficit under the

‘NoBFD’, where biomass use is not modelled as mandatory, the imports are projected to be

lower in all scenarios, see figure 4.

Figure 4: Trade balance in biomass crops, in bln €, Netherlands

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0


Initial 2010 2020 2030 2030, NoBFD

The Dutch balance in trade with biomass crops also declines, see Figure 4, which

presents the development of the aggregated Dutch trade balance in biomass crops,

regardless of whether these products are used for food, feed purposes or as inputs

in the bio-based industries. Under the two ‘Int’ scenarios (with open trade with all

trading partners), Dutch biomass imports increase and the trade balance becomes

more negative, especially in the IntHighTech scenario, where biomass use is high.

This strong increase is also due to relative high biomass blending shares modelled

especially under the IntHighTech and IntHighTechAC scenarios. With the lower

trade deficit under the ‘NoBFD’, where biomass use is not modelled as mandatory,

the imports are projected to be lower in all scenarios, see Figure 4.


Figure 4: Trade balance in biomass crops, in bln €, the Netherlands

11

-90.0

-80.0

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0


Initial 2010 2020 2030 2030, NoBFD

Figure 3: Balance in total trade, in bln €, Netherlands

The Dutch balance in trade with biomass crops also declines, see figure 4. Figure 4 presents

the development of the aggregated Dutch trade balance in biomass crops regardless whether

these products are used for food, feed purposes or as inputs in the bio-based industries. Under

the two ‘Int’ scenarios with open trade with all trading partners, Dutch biomass imports

increases and the trade balance becomes more negative, especially in the IntHighTech

scenario in which biomass use is high.

This strong increase is also due to relative high biomass blending shares modelled especially

under the IntHighTech and IntHighTechAC scenarios. The lower trade deficit under the

‘NoBFD’, where biomass use is not modelled as mandatory, the imports are projected to be

lower in all scenarios, see figure 4.

Figure 4: Trade balance in biomass crops, in bln €, Netherlands

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0


Initial 2010 2020 2030 2030, NoBFD

The regional composition of trade in biomass crops is depicted in Figures 5 and 6.

Figure 5 presents the development under the NatLowTech scenario, which serves as

a kind of reference scenario between the initial period and 2030. The projection

indicates that the majority of biomass crop imports is coming from the other EU

Member States as well as from North and South America. With an increase in

imports of biomass crops, the additional demand for biomass will come mainly from

those regions with a relative large land reserve. Under all model scenarios the land

reserve in EU-15 countries is rather limited, while in the North American countries

land reserves are higher compared to the EU-15 Member States. The largest reserve

in agricultural land is projected for the countries in South America. Consequently,

additional demand for biomass crops imported to the Netherlands is projected to

come from South American countries, especially Brazil.

Under the ‘International’ scenario it is assumed that biomass imports come

particularly from the non-EU countries. Technically this assumption is

implemented with higher trade elasticities for the ‘International’ scenarios than for

the ‘National’ scenarios. Therefore, imports from outside EU are not restricted

under the ‘National’ scenarios. Due to high trade elasticities under the

‘International’ scenarios producers strongly react to relative changes in domestic

vs. world prices. This set-up also explains the strong increase in non-EU imports

under the IntHighTech scenario.


Figure 5: Imports of biomass crops under NatLowTech, in mln €, the Netherlands

12

The regional composition of trade in biomass crops is presented in the following figures 5

and 6. Figure 5 presents the development under the NatLowTech scenario, which serves as a

kind of reference scenario between the initial period and 2030. The projection indicates that

the major part of biomass crop imports is coming from the other member states of the EU as

well as North America and South America. With an increase in imports in biomass crops, the

additional demand for biomass will come mainly from those regions with a relative large land

reserve. Under all model scenarios the land reserve in EU-15 countries is rather limited,

while in the North American countries land reserves are higher compared to the EU-15

member states. The largest reserve in agricultural land is projected for the countries in South

America. Consequently additional demand for biomass crops imported to the Netherlands is

projected to come from South American countries, especially Brazil.

Under the ‘International’ it is assumed that biomass imports comes especially from the non-

EU countries. Technically this assumption is implemented with higher trade elasticities for

the ‘International’ scenarios than for the ‘National’ scenarios. Therefore, imports from

outside EU are not restricted under the ‘National’ scenarios. Due to high trade elasticities

under the ‘International’ scenarios producers strongly react to relative changes in domestic

vs. world prices. This set-up also explains the strong increase in non-EU imports under the

IntHighTech scenario.

Figure 5: Imports of biomass crops under NatLowTech, in mln €, Netherlands

While figure 5 shows the development of total biomass crop imports, the composition of the

utilization of these crops within the Dutch economy significantly changes over time. In the

initial situation biomass imports, as an input to the bio-based economy, contributes only to

around 3% of total imports of these products. In 2030, however, almost half of the total

biomass imports are used as an input to the bio-based sectors in the Netherlands.

The differences in biomass imports between the different scenarios, presented in figure 6, are

due to the different blending shares in the scenarios calculated for this study. In the

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

Initial 2010 2020 2030 2030,no

BFD

EU27 Africa Asia NorthAm SouthAm RoW

While Figure 5 shows the development of total biomass crop imports, the

composition of the utilisation of these crops within the Dutch economy significantly

changes over time. In the initial situation biomass imports, as an input to the bio-

based economy, contribute only around 3% of total imports of these products. In

2030, however, almost half of the total biomass imports are used as an input to the

bio-based sectors in the Netherlands.

The differences in biomass imports between the different scenarios, presented in

Figure 6, are due to the different blending shares in the scenarios calculated for

this study. In the IntHighTech and IntHighTechAC scenarios, with blending shares

of 60% and 75%8 in transportation fuels, respectively, imports of biomass are

projected to increase to more than 5 billion €. Most of this additional import is

coming from South American countries, see Figure 6.

8 TheGTAP-basedmodelaggregatesallpetroleumproductsinonesector(Petrol).Becauseboth

transportfuelsaschemicalfeedstocksareaggregatedtoonecommodity,replacementofnaphtha

forethyleneproductionbybiomassimpliesahigherbiomassblendingshareinthePetrolsector.

Thisistranslatedinanadditionalbio-basedblendingshare(15%points),i.e.demandforethanolfor

transportfuelsintheLEITAPmodel.


Figure 6: Imports of biomass crops in 2030 under different scenarios, in mln €, the Netherlands

13

IntHighTech and IntHighTechAC scenarios, with blending shares of 60% and 75%8 in

transportation fuels, respectively, imports of biomass is projected to increase to more than 5

billion €. Most of this additional imports is coming from South American countries, see

figure 6.

Figure 6: Imports of biomass crops in 2030 under different scenarios, in mln €,

Netherlands

Figure 7a shows the development of the total production of biomass crops (grain, oilseeds

and woody crops) regardless of final uses (food, feed, and biomass) under the four main

scenarios. It becomes clear that even under high blending rates – projected under the

IntHighTech scenarios, Dutch agriculture does not expand production at a high rate. This

little expansion is due to restrictions on agricultural land use in the Netherlands. The higher

biomass use under mandatory blending will lead to an additional crop production of around

150 million €. As already mentioned for the development of imports of biomass crops, the

use of biomass crops in the bio-based industries increases even without mandatory blending.

This endogenous development is due to the fact that until 2030 the development of relative

prices is in favour for biomass crops, i.e. prices for biomass crops are projected to decline

relative to fossil energy prices. Therefore, the use of biomass inputs as a substitute for fossil

energy becomes more and more profitable.

Figure 7b – the right hand graph – presents the share of domestic crops used in the biobased

industry in total domestic crops production. It should be mentioned that these numbers are

relative to initial 2006 values. Showing the development relative to the initial situation

8 The GTAP based model aggregates all petroleum products in one sector (Petrol). Because both transport fuels as chemical feedstocks are aggregated to one commodity, replacement of naphtha for ethylene production by biomass implies a higher biomass blending share in the Petrol sector. This is translated in an additional biobased blending share (15% points), i.e. demand for ethanol for transport fuels in the LEITAP model.

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0


EU27 Africa Asia NorthAm SouthAm RoW

Figure 7a shows the development of the total production of biomass crops (grain,

oil-seeds and woody crops) regardless of final uses (food, feed, and biomass) under

the four main scenarios. It becomes clear that even under high blending rates –

projected under the IntHighTech scenarios, Dutch agriculture does not expand

production at a high rate. This small expansion is due to restrictions on agricultural

land-use in the Netherlands. The higher biomass use under mandatory blending will

lead to an additional crop production of around 150 million €. As already mentioned

for the development of imports of biomass crops, the use of biomass crops in the

bio-based industries increases even without mandatory blending. This endogenous

development is due to the fact that, until 2030, the development of relative prices is

in favour for biomass crops, i.e. prices for biomass crops are projected to decline

relative to fossil energy prices. Therefore, the use of biomass inputs as a substitute

for fossil energy becomes more and more profitable.

Figure 7b – the right-hand graph – presents the share of domestic crops used in the

bio-based industry in total domestic crops production. It should be mentioned that

these numbers are relative to initial 2006 values. Showing the development relative

to the initial situation presents both the autonomous trend to use more biomass and

the enforced biomass use due to mandatory blending targets. As a general result:

2/3 of the biomass crop demand is related to mandatory targets, while 1/3 is related

to autonomous trends which occur also without mandatory blending targets.


Figure 7a: Production of biomass crops, in bln €, Figure 7b: Share of biomass crops use

the Netherlands for bio-based Industries, in %

14

presents both the autonomous trend to use more biomass and the enforced biomass use due to

mandatory blending targets. As a general result: 2/3 of the biomass crop demand is related to

mandatory targets while 1/3 is related to autonomous trends which occur also without

mandatory blending targets.

Figure 7a: Production of biomass crops,

in bln €, Netherlands

Figure 7b: Share of biomass crops use for

bio-based Industries, in %

The share of domestically produced biomass crops in total biomass crop production strongly

depends on the assumed blending target. Under the IntHighTechAC scenario around 37% of

total Dutch biomass crop production is projected to be used as inputs to the bio-based

industry, Figure 7b.

Figure 8a: Income in bio-based industries,

in mln €, Netherlands

Figure 8b: Share of employment in bio-

based industries, in %

0%

5%

10%

15%

20%

25%

30%

35%

40%

NatLowTech IntLowTech NatHighTech IntHighTech IntHighTechAC

2010 2020 2030

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0


Initial 2010 2020 2030 2030, no BFD

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0


Initial 2010 2020 2030 2030, NoBFD

0%

2%

4%

6%

8%

10%

12%

14%


Initial 2010 2020 2030

The share of domestically produced biomass crops in total biomass crop production

strongly depends on the assumed blending target. Under the IntHighTechAC

scenario around 37% of total Dutch biomass crop production is projected to be used

as inputs to the bio-based industry, Figure 7b.

Figure 8a: Income in bio-based industries, in mln €, Figure 8b: Share of employment in bio-based

the Netherlands industries, in %

14

presents both the autonomous trend to use more biomass and the enforced biomass use due to

mandatory blending targets. As a general result: 2/3 of the biomass crop demand is related to

mandatory targets while 1/3 is related to autonomous trends which occur also without

mandatory blending targets.

Figure 7a: Production of biomass crops,

in bln €, Netherlands

Figure 7b: Share of biomass crops use for

bio-based Industries, in %

The share of domestically produced biomass crops in total biomass crop production strongly

depends on the assumed blending target. Under the IntHighTechAC scenario around 37% of

total Dutch biomass crop production is projected to be used as inputs to the bio-based

industry, Figure 7b.

Figure 8a: Income in bio-based industries,

in mln €, Netherlands

Figure 8b: Share of employment in bio-

based industries, in %

0%

5%

10%

15%

20%

25%

30%

35%

40%


2010 2020 2030

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0


Initial 2010 2020 2030 2030, no BFD

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0


Initial 2010 2020 2030 2030, NoBFD

0%

2%

4%

6%

8%

10%

12%

14%


Initial 2010 2020 2030

Under all scenarios the aggregated income in the petrol, electricity and the fine

chemical industries is projected to increase, see Figure 8a. This figure includes the

income generated in both the ‘non bio-based’ and the bio-based part of the

respective industries.9 Amongst the three bio-based sectors covered in this study,

under the IntHighTech scenario 75% of the total income presented in Figure 8a is

allocated to the electricity sector, 24% for the petrol and 1% to the fine chemicals

9 Sectoralincomeisdefinedasthesumofallpaymentstofactorsemployedineachsector.These

paymentsincludesalaries,capitalusercostsandlandrentswhichoccurinagricultureonly.Please

notethatinstallationandinvestmentscostsofnewtechnologiesarenotincludedintheseincome

figures,displayedhere.


sector.10 In the IntHighTechAC scenario these shares change and only 60% of total

income is generated in the electricity sector, 30% in the petrol sector and 10% in the

chemical sector.

Higher blending rates will lead to an increase in biomass inputs at the expense of

fossil energy inputs. The questions remains: Will this development towards a more

bio-based economy also lead to an increased income in the bio-based industries?

The scenario results indicate that, with a shift towards a more bio-based economy,

total income in the bio-based sectors might be up to 1 billion € higher compared to

scenarios without an enforced use of biomass, see Figure 8a. This strong income

effect is projected only for the IntHighTech scenario, which assumes very high

utilisation of biomass in 2030 under very favourable conditions. Under a more

conservative scenario, assuming a low rate of technology development, such as the

IntLowTech scenario, the projected additional income is only 100 million €.

Although, depending on the scenario, 60-75% of total income is allocated to

electricity, the additional income from enhanced bio-based activities industries is

projected to be allocated differently. Of the 1 billion € additional income under the

IntHighTech scenario, 19% is created in the electricity production, 78.5% in petrol

production and 2.5% in the (fine) chemicals industries. Under the IntHighTechAC

scenario the shares are 11% in electricity, 76% in the petrol and 14% in the chemical

sectors.11 These numbers are different to the bottom-up analysis conducted in Part I

of this report. The structure of employment in the bio-based sectors is also affected

by the shift towards a more bio-based oriented economy. With high blending shares

under the IntHighTech scenario, almost 12% of total employment in the petrol, fine

chemical and electricity sector is working in the bio-based part of these industries,

see Figure 8b. With 13.8% under the IntHighTechAC scenario, this share is even

higher.

Similar developments are projected for the additional income for Dutch agriculture.

Compared to the scenarios without enforced biomass use (NoBFD scenario), the

enhanced biomass use generates an additional income between 50 and 140 million €

in Dutch farming, see Figure 9a. This development is also mirrored by the share of

agricultural employment in the production of biomass crops, see Figure 9b.

Depending on the projected scenario, between 3% and 5% of agricultural employment

will be related to the production of biomass crops used in the bio-based sectors.

It is important to mention that total agricultural employment is projected to decline

10 Thehighshareinelectricityisduetothefactthattheelectricitysectorinthetop-downmodel

includesbothelectricityproductionanddistribution.

11 ThislowershareintheIntHighTechACscenario(comparedtotheIntHighTechscenario)isrelated

tothefactthatthesubstitutionofnaturalgasbysyngasmainlyaffectsthegassectorandnotthe

chemicalindustries.Biobasedsynthesisgasrequireshighcapitalinvestmentsandskilledlabour

similarto2ndgenerationbiofuels.Theseresultsindicatethatfurtherresearchisrequiredinorderto

addressforthesefactorsinthemacro-economicmodel.


strongly in all scenarios. Compared to current levels, employment in Dutch

agriculture in 2030 is projected to be half the current level. This strong decline is

mainly due to a high growth in labour productivity, which boosts the structural

change in Dutch farming. The projected increasing share of employment for biomass

crops the bio-based economy is not able to alter this trend, but it will ease the

burden of structural changes in Dutch agriculture.

Figure 9a: Agricultural income in biomass production, Figure 9b: Share of employment in biomass

in mill. €, Netherlands production, in %

16

3% and 5% of agricultural employment will be related to the production of biomass crops

used in the bio-based sectors.

It is important to mention that total agricultural employment is projected to decline strongly

in all scenarios. Compared to current level employment in Dutch agriculture in 2030 is

projected to be half of current level. This strong decline is mainly due to a high growth in

labor productivity which boosts the structural change in Dutch farming. The projected

increasing share of employment for biomass crops the bio-based economy is not able to alter

this trends but it will ease the burden of structural changes in Dutch agriculture.

Figure 9a: Agricultural income in biomass

production, in mill. €, Netherlands

Figure 9b: Share of employment in

biomass production, in %

The development of the cost structure of the Dutch petrol industry under the NatLowTech

scenario is presented in figure 10. This figure describes total production of the Dutch petrol

industries, i.e. production for domestic use and for exportation. Please note that the blending

shares are assumed for the entire EU and that exported petrol also fulfills the blending

requirements.

While biomass use is relatively low in the initial situation the relative importance grows until

2030. Even without enforced biomass use the utilization of biomass increases due to the

change in relative prices between biomass and fossil inputs. The question, whether petrol is

mainly based on fossil energy inputs (as modeled in NoBFD scenarios) or produced with

higher biomass shares has only limited impact on the total value added generated in the petrol

sector, (compare last two columns in figure 10).

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0


Initial 2010 2020 2030 2030, NoBFD

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%


Initial 2010 2020 2030

The development of the cost structure of the Dutch petrol industry under the

NatLowTech scenario is presented in Figure 10. This figure describes total

production of the Dutch petrol industries, i.e. production for domestic use and for

exportation. Please note that the blending shares are assumed for the entire EU and

that exported petrol also meets the blending requirements.

While biomass use is relatively low in the initial situation the relative importance

grows until 2030. Even without enforced biomass use the utilisation of biomass

increases due to the change in relative prices between biomass and fossil inputs.

The question, if petrol is mainly based on fossil energy inputs (as modelled in

NoBFD scenarios) or produced with higher biomass shares, this has only limited

impact on the total added-value generated in the petrol sector, (compare the last two

columns in Figure 10).


Figure 10: Cost structure in petrol sector, in bln €, the Netherlands under NatLowTech scenario

17

Figure 10: Cost structure in petrol sector, in bln €, Netherlands

under NatLowTech scenario

With relatively low blending shares under the NatLowTech scenario the share of biomass use

significantly increases in different scenarios and under the IntHighTech (IntHighTechAC)

scenario, 60% (75%) of transportation fuel is based on biomass inputs, figure 11. Please note

that the shares presented in figures 10 and 11 refer to the value share of the respective inputs

and not on the volume share.

Figure 11: Cost structure in petrol sector under different scenarios, in 2030 in bln €,

Netherlands

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

Initial 2010 2020 2030 2030,

NoBFD

value added other inputs fossil energy biomass inputs

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0



With relatively low blending shares under the NatLowTech scenario the share of

biomass use significantly increases in different scenarios and under the

IntHighTech (IntHighTechAC) scenario, 60% (75%) of transportation fuel is based on

biomass inputs, see Figure 11. Please note that the shares presented in Figures 10

and 11 refer to the value share of the respective inputs and not to the volume share.

Figure 11: Cost structure in petrol sector under different scenarios, in 2030 in bln €, the Netherlands

17

Figure 10: Cost structure in petrol sector, in bln €, Netherlands


With relatively low blending shares under the NatLowTech scenario the share of biomass use

significantly increases in different scenarios and under the IntHighTech (IntHighTechAC)

scenario, 60% (75%) of transportation fuel is based on biomass inputs, figure 11. Please note

that the shares presented in figures 10 and 11 refer to the value share of the respective inputs

and not on the volume share.

Figure 11: Cost structure in petrol sector under different scenarios, in 2030 in bln €,

Netherlands

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

Initial 2010 2020 2030 2030,

NoBFD


0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0




The Dutch petrol industry is highly integrated into the single European market and

a large share of crude oil, which is imported, then processed in the Netherlands,

before being exported to other EU Member States. The following two graphs

illustrate the trade structure of the Dutch petrol sector under the NatLowTech

scenario.

Figure 12a: Trade in crude oil and petrol, Figure 12b: Production and exports in petrol,

in mill. €, the Netherlands (NatLowTech scenario) in mill. €, the Netherlands (NatLowTech scenario)

18

The Dutch petrol industry is highly integrated in the single European market and a large share

of crude oil, which is imported, is processed in the Netherlands, but exported to other EU

member states afterwards. The following two graphs illustrate the trade structure of the Dutch

petrol sector under the NatLowTech scenario.

Figure 12a: Trade in crude oil and petrol,

in mill. €, Netherlands (NatLowTech

scenario)

Figure 12b: Production and exports in

petrol, in mill. €, Netherlands

(NatLowTech scenario)

Figure 12a shows the large trade surplus in Dutch petrol trade. Around 50% of total Dutch

petrol production is exported after processing, see figure 12b. Figure 12a shows that imports

and exports of oil and petroleum products increase at almost the same magnitude. Therefore

the strong increase in the Dutch trade deficit, as described above, is not related to the petrol

sector.

Figure 13 illustrates the composition of biomass inputs in the Dutch petrol sector in term of

energy value of the various inputs. These numbers show how the estimated amount of

biomass inputs from the bottom-up process are integrated in the macro-economic model. The

total amount of biofuel crops is determined by two factors: a) the autonomous trend towards a

bio-based economy and b) by an enforced biomass use due to blending targets of biomass

use. The first effect is induced by the change in the relative prices between biomass and fossil

energy.12

As outlined already in the report for the bottom-up approach, the composition is changing.

Under the LowTech scenarios 1st generation biomass crops (domestic and imported)

dominate the use of biomass crops, while under the HighTech Scenarios the share of 2nd

generation biomass (woody-crops) becomes more important. These numbers are also

reflected by the outcome of the macro-economic model; see first two columns in figure 13a.

12 The average change in relative prices between (aggregated) biomass and fossil energy under the NoBFD scenarios is around -30% under the NatLowTech scenario.

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

Initial 2010 2020 2030

Crude Oil, Imports Crude Oil, Exports

Petrol, Imports Petrol, Exports

0

2000

4000

6000

8000

10000

12000

14000

Initial 2010 2020 2030

Production Exports, EU Exports, NonEU

Figure 12a shows the large trade surplus in Dutch petrol trade. Around 50% of total

Dutch petrol production is exported after processing, see Figure 12b. Figure 12a

also shows that imports and exports of oil and petroleum products increase at

almost the same magnitude. Therefore the strong increase in the Dutch trade deficit,

as described above, is not related to the petrol sector.

Figure 13 illustrates the composition of biomass inputs in the Dutch petrol sector in

terms of energy value of the various inputs. These numbers show how the estimated

amount of biomass inputs from the bottom-up process are integrated into the

macro-economic model. The total amount of biofuel crops is determined by two

factors: a) the autonomous trend towards a bio-based economy and b) by an

enforced biomass use due to blending targets of biomass use. The first effect is

induced by the change in the relative prices between biomass and fossil energy.12

As outlined already in the report concerning the bottom-up approach, the

composition is changing. Under the LowTech scenarios 1st-generation biomass crops

(domestic and imported) dominate the use of biomass crops, while under the

HighTech Scenarios the share of 2nd-generation biomass (woody-crops) becomes

more important. These numbers are also reflected by the outcome of the macro-

economic model; see first two columns in Figure 13a. Figures 13b and 13c show the

composition of biomass inputs in the electricity and the fine chemical sectors,

respectively.

12 Theaveragechangeinrelativepricesbetween(aggregated)biomassandfossilenergyunderthe

NoBFDscenariosisaround-30%undertheNatLowTechscenario.


Figure 13a: Composition of biomass inputs in petrol sector, in 2030 in PJ, Netherlands

19

Figures 13b and 13c show the composition of biomass inputs in the electricity and the fine

chemical sectors, respectively.


It should be noted that – as already explained above – ethanol is not modeled as an individual

product in the current version of LEITAP. Therefore, the increase use of sugar under the both

HighTech scenario may be interpreted as a proxy for the increase in ethanol production based

on 1st generation crops. Comparing the outcome of the macro-economic model with the

estimate of the bottom-up approach one could expect a larger share of 2nd generation

biomass, especially for the HighTech scenario. Under the IntHighTech scenario oilseeds still

contribute a significant part to total biomass use in the petrol industry.

Figure 13b: Composition of biomass inputs in

electricity, in 2030 in PJ,

Netherlands

Figure 13c: Composition of biomass inputs in

fine chemicals, in 2030 in PJ,

Netherlands

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0


Grain Sugar Woody crops Veget. Oils

0

20

40

60

80

100

120

140

NatLowTech IntLowTech NatHighTech IntHighTech


0

10

20

30

40

50

60

70

80


Grain Sugar Woody crops

It should be noted that – as already explained above – ethanol is not modelled as an

individual product in the current version of LEITAP. Therefore, the increased use of

sugar under the both HighTech scenario may be interpreted as a proxy for the

increase in ethanol production based on 1st-generation crops. Comparing the

outcome of the macro-economic model with the estimate of the bottom-up approach

one could expect a larger share of 2nd-generation biomass, especially for the

HighTech scenario. Under the IntHighTech scenario oil-seeds still contribute a

significant amount to total biomass use in the petrol industry.

Figure 13b: Composition of biomass inputs in electricity, Figure 13c: Composition of biomass inputs in fine

in 2030 in PJ, the Netherlands chemicals, in 2030 in PJ, the Netherlands

19

Figures 13b and 13c show the composition of biomass inputs in the electricity and the fine

chemical sectors, respectively.


It should be noted that – as already explained above – ethanol is not modeled as an individual

product in the current version of LEITAP. Therefore, the increase use of sugar under the both

HighTech scenario may be interpreted as a proxy for the increase in ethanol production based

on 1st generation crops. Comparing the outcome of the macro-economic model with the

estimate of the bottom-up approach one could expect a larger share of 2nd generation

biomass, especially for the HighTech scenario. Under the IntHighTech scenario oilseeds still

contribute a significant part to total biomass use in the petrol industry.

Figure 13b: Composition of biomass inputs in

electricity, in 2030 in PJ,

Netherlands

Figure 13c: Composition of biomass inputs in

fine chemicals, in 2030 in PJ,

Netherlands

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0



0

20

40

60

80

100

120

140



0

10

20

30

40

50

60

70

80


Grain Sugar Woody crops

This outcome is explained by the underlying technology assumption of the LEITAP

model. Due to the fact that technology changes follow a path of substituting an

existing technology (based on 1st-generation biomass) with a new and modern one

(based on 2nd-generation biomass) the model seems to react a bit ‘sticky’. Thus,

LEITAP does not allow for drastic changes in the composition of the feedstock in the


biomass sector.13 Thus, even in the IntHighTech scenario, 1st-generation biomass

crops, such as oil-seeds, continue to contribute to biofuel production at a significant

level. Based on the economic model applied for this study, the achieved results

indicate that an economy fully based on 2nd-generation biomass inputs would

require a longer timeframe for adjustment.

Due to the remaining use of 1st-generation biomass crops even under the HighTech

scenario some subsidies are still necessary to meet the blending target. The

‘persistent’ contribution of 1st-generation biomass also has consequences for the

calculation of social costs of an enforced utilisation of biomass crops in the bio-

based industries, see Table 1. The compositions of biomass use in the two other bio-

based industries – electricity and fine chemicals – are more biased towards a single

input (woody crops) in electricity, and a mix of sugar and woody crops in fine

chemical industries.

Similar to the development of biomass use in the petrol sector (Figure 10), the

electricity sector uses only a small amount of biomass inputs under the NatLowTech

scenario, see Figure 14. In 2030 it is assumed that 5.7% of total energy inputs in the

Dutch electricity production are based on biomass inputs. It should also be

mentioned that the composition of the electricity sector differs significantly from

the petrol sector. In the electricity sector value added represents the largest cost

shares, with more than 50% in total costs, which also contributes to the large share

of electricity in the aggregated bio-based sectors.

Figure 14: Cost structure in electricity sector, in bln €, the Netherlands under NatLowTech scenario

20

This outcome is explained by the underlying technology assumption of the LEITAP model.

Due to the fact that technology changes follow a path of substituting an existing technology

(based on 1st generation biomass) with a new and modern one (based on 2nd generation

biomass) the model seems to react a bit ‘sticky’. Thus, LEITAP does not allow for drastic

changes in the composition of the feedstock in the biomass sector.13 Thus, even in the

IntHighTech scenario, 1st generation biomass crops such as oilseeds continue to contribute to

biofuel production at a significant level. Based on the economic model applied for this study

the achieved results indicate that an economy fully based on 2nd generation biomass inputs

would require a longer time path for adjustment.

Due to the remaining use of 1st generation biomass crops even under the HighTech scenario

some subsidies are still necessary to fulfill the blending target. The ‘persistent’ contribution

of 1st generation biomass has also consequences for the calculation of social costs of an

enforced utilization of biomass crops in the bio-based industries, see following table 1. The

composition biomass use in the two other biobased industries – electricity and fine chemicals

– are more biased to a single input (woody crops) in electricity and a mix of sugar and woody

crops in fine chemical industries.

Similar to the development of biomass use in the petrol sector (figure 10) the electricity

sector uses only a small amount of biomass inputs under the NatLowTech scenario, see figure

14. In 2030 it is assumed that 5.7% of total energy inputs in the Dutch electricity production

are based on biomass inputs. It should be also mentioned that the composition of the

electricity sector differs significantly from the petrol sector. In the electricity sector value

added has the largest cost shares with more than 50% in total costs which also contributes to

the large share of electricity in the aggregated bio-based sectors.

Figure 14: Cost structure in electricity sector, in bln €, Netherlands


13 Other modelling approaches such as a linear-programming model would allow for these immediate shifts in the mix of 1st and 2nd generation biomass. However, these modeling approaches would neglect other important features such as the endogenous development of relative prices between different inputs.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Initial 2010 2020 2030

value added other inputs fossil energy biofuel inputs

For the other three scenarios, biomass inputs will not dominate the demand of

13 Othermodellingapproachessuchasalinear-programmingmodelwouldallowfortheseimmediate

shiftsinthemixof1st-and2nd-generationbiomass.However,thesemodellingapproachesneglect

otherimportantfeaturessuchastheendogenousdevelopmentofrelativepricesbetweendifferent

inputs.


energy-related inputs in the electricity sector, see Figure 15. Under the IntHighTech

scenario, biomass inputs are assumed to contribute by almost 1/3 to total energy-

related input demand.

Figure 15: Cost structure in electricity sector under different scenarios, in 2030 in bln €,

the Netherlands

21

For the other three scenarios biomass inputs will not dominate the demand of energy related

inputs in the electricity sector, see figure 15. Under the IntHighTech scenario biomass inputs

are assumed to contribute by almost 1/3 to total energy related input demand.

Figure 15: Cost structure in electricity sector under different scenarios, in 2030 in bln

€, Netherlands

The following table 1 presents the burden to tax payers of an enforced use of biomass in the

Dutch petrol industry. With around 14 billion liters, the amount of total petrol consumed is

very similar between the different scenarios and the differences are due to different prices for

gasoline. However, with different blending rates the amount of biofuels (including the

naphtha substitution) produced in 2030 varies from 1.4 billion in the NatLowTech to 8.6

billion liters in the IntHighTech scenario and 10.6 billion liters under the additional

IntHighTechAC scenario.

Table 1: Tax burden of using biomass inputs for liquid petrol production, 2030 in

the Netherlands

NatLowTech IntLowTech NatHighTech IntHighTech IntHighTech

AC

Fuel consumption (mill. liters) 14218 14035 14364 14286 14160

Substitution share in % 10 20 20 60 75

Amount of biofuel (mill. liters) 1422 2807 2873 8572 10620

Subsidies, million € 578 347 828 293 421

€/liter of biofuel 0.407 0.124 0.288 0.034 0.040

With these different volumes of biofuels produced also the absolute spending in subsidies is

very different and depends on the assumed technology. The highest spending on subsidies to

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0


value added other inputs fossil energy biofuel inputs

Table 1 presents the burden to taxpayers of an enforced use of biomass in the Dutch

petrol industry. With around 14 billion litres, the amount of total petrol consumed

is very similar between the different scenarios and the differences are due to

different petrol prices. However, with different blending rates the amount of

biofuels (including the naphtha substitution) produced in 2030 varies from 1.4

billion in the NatLowTech to 8.6 billion litres in the IntHighTech scenario, and 10.6

billion litres under the additional IntHighTechAC scenario.

Table 1: Tax burden of using biomass inputs for liquid petrol production, 2030 in the Netherlands


Fuel consumption (mill. litres) 14218 14035 14364 14286 14160

Substitution share in % 10 20 20 60 75

Amount of biofuel (mill. litres) 1422 2807 2873 8572 10620

Subsidies, million € 578 347 828 293 421

€/litre of biofuel 0.407 0.124 0.288 0.034 0.040

With these different volumes of biofuels produced, the absolute spending in

subsidies is also very different and depends on the assumed technology. The highest

spending on subsidies to compensate petrol producers for the (otherwise)

unprofitable product is projected for the NatHighTech scenario where annual

taxpayers’ burden of biomass use in the petrol industry is more than 800 million €.

To compare the costs across different scenarios we calculate the required subsidies


of producing 1 litre of biofuel. The results show a strong decline in subsidies per

litre of biofuel in moving from LowTech to HighTech scenarios and in moving from

national to international scenarios. As discussed already for Figure 13, under the

IntHighTech scenario, subsidies of 0.034 €/litre of biofuel are still required due to

the (remaining but declining) use of 1st-generation biomass crops. Similar results

are achieved for the IntHighTechAC scenario, where blending rates are at the 75%

level. Here the required subsidy per litre of biofuel is 0.04 €/litre, which is higher

compared to the IntHighTech scenario. The questions (whether 2nd-generation

biomass crops still needs subsidies to be profitable compared to fossil energy)

strongly depends on the technology and the prices for fossil energy. Under the very

optimistic assumptions of the IntHighTech scenario the scenario results indicate

that 2nd-generation biomass becomes competitive at a price level of 75 US$/bbl (in

2006 US$).

Results of the Sensitivity Analysis

Because the market for bioenergy and bio-based materials is surrounded by

uncertainties, the sensitivity analysis shows the impact of the most crucial

assumption for this study: the development of the world market price of fossil

energy. Two additional scenarios have been calculated with regard to the

development of world fossil energy prices. Under the scenario IntHighTechHigh, the

increase in fossil energy prices is 50% (which is 112 USD/bbl, in 2006 USD) higher

than in the reference IntHighTech scenario. Under the scenario IntHighTechLow,

fossil energy prices are assumed to be 25% lower compared to the IntHighTech

scenario, which is 56 USD/bbl, in 2006 USD. To identify the impact of different

developments of fossil energy prices in the IntHighTech scenario all other

assumptions, including the blending rates of biomass utilisation in the bio-based

sectors, are kept unchanged in the two sensitivity scenarios.14 The following graphs

only show those results that indicate a significant difference in the results of the

two sensitivity scenarios compared with the IntHighTech scenario.

Under higher fossil energy prices the Dutch trade balance will further deteriorate

due to higher expenses for energy import, while lower energy prices will lower the

Dutch trade deficit, see Figures 16a and 16b.

Figure 16a: Sensitivity scenarios: Balance in total trade, Figure 16b: Sensitivity scenarios: Balance in

14 Itshouldbementionedthat,duetolimitedamountoftimeandspace,thesensitivityscenarios

havebeencalculatedonlyfortheIntHighTechscenarioandnotforallotherthreemainscenarios

presentedinthepreviouschapter.


in bln €, the Netherlands biomass crop trade, in bln €, the Netherlands

23

Figure 16a: Sensitivity scenarios: Balance in total

trade, in bln €, Netherlands

Figure 16b: Sensitivity scenarios: Balance

in biomass crop trade, in bln €,

Netherlands

Under the IntHighTechLow sensitivity scenario the Dutch balance in trade with biomass

crops change only little relative to the IntHighTech scenario, see figure 16b. However, under

higher fossil oil prices more biomass crops will be used compared to the IntHighTech

scenario, i.e. with higher fossil energy prices biomass use become more profitable. This result

indicates that with lower fossil energy prices, the blending shares applied in the scenarios

remain ‘binding’ constrains. The lower profitability of biomass crops – due to lower fossil

energy prices – will lead to higher subsidies on biomass inputs, even under the IntHighTech

scenario, see below.

Due to the limited availability of agricultural land, Dutch agriculture does not benefit from

this increase in biomass demand in the bio-based sectors. The additional demand is covered

almost completely by an increase in biomass imports, with South America as the most

important origin of imports.

The increase in demand of biomass crops in the biobased sectors positively affects income

generated in those sectors, see figure 17. Under the IntHighTechHigh scenario, total income

in the bio-based industries is around 800 million € higher compared to the IntHighTech

scenario. Lower energy prices lowers total income in the bio-based sector which is due to

changes in the relative factor prices.

-100.0

-90.0

-80.0

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

IntHighTech IntHighTechHigh IntHighTechLow

Initial 2010 2020 2030

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0


Initial 2010 2020 2030

Under the IntHighTechLow sensitivity scenario the Dutch balance in trade with

biomass crops change only little relative to the IntHighTech scenario, see Figure

16b. However, with higher fossil oil prices, more biomass crops will be used

compared to the IntHighTech scenario, i.e. with higher fossil energy prices biomass

use becomes more profitable. This result indicates that with lower fossil energy

prices, the blending shares applied in the scenarios remain ‘binding’ constraints.

The lower profitability of biomass crops – due to lower fossil energy prices – will

lead to higher subsidies on biomass inputs, even under the IntHighTech scenario,

see below.

Due to the limited availability of agricultural land, Dutch agriculture does not

benefit from this increase in biomass demand in the bio-based sectors. The

additional demand is almost entirely covered by an increase in biomass imports,

with South America as the most important origin for imports.

The increase in demand of biomass crops in the bio-based sectors positively affects

income generated in those sectors, see Figure 17. Under the IntHighTechHigh

scenario, total income in the bio-based industries is around 800 million € higher

compared to the IntHighTech scenario. Lower energy prices lowers total income in

the bio-based sector, which is due to changes in the relative factor prices.


Figure 17: Sensitivity scenarios: Income in bio-based industries, in mln €, the Netherlands

2

4

Figure 17: Sensitivity scenarios: Income in bio-based industries, in mln €, the

Netherlands

With a higher demand for biomass inputs in the bio-based industries the composition of

biomass remains unchanged. However, the level of fossil energy prices determines the

competitiveness of biomass inputs relative to fossil inputs in the bio-based sectors. The lower

the fossil prices the more ‘costly’ is the use of biomass inputs. Without blending shares

which are set as minimum blending requirements for the bio-based sectors, less biomass

would be used, i.e. additional subsidies are required to maintain the blending shares at their

minimum level. Higher fossil energy prices increase the relative competitiveness of biomass

inputs. The sensitivity analysis shows that under the IntHighTech scenario with high fossil

energy prices the required subsidies become very low, see Table 2.

Table 2: Sensitivity analysis: Tax burden of using biomass inputs for liquid petrol

production, 2030 in the Netherlands


Fuel consumption (mill. litres) 14286 13286 14786

Substitution share in % 60 62 60

Amount of biofuel (mill. litres) 8572 8237 8872

Subsidies, million € 293 121 521

€/litre of biofuel 0.034 0.015 0.059

The sensitivity analyses shows that the qualitative results are not fundamentally different, but

the extent of the effects can change substantially. The sensitivity analysis shows that, despite

the ambitious biomass blending targets in the IntHighTech scenario, at crude oil prices of 112

USD/bbl, biomass becomes competitive and increases the demand for bioenergy crops. The

results, i.e. bio-based production, however are still in line with the baseline situation. The

required subsidies for bio-based substitution of fossil energy carriers is sensitive to fossil

energy prices as displayed in Table 2.

With a higher demand for biomass inputs in the bio-based industries the

composition of biomass remains unchanged. However, the level of fossil energy

prices determines the competitiveness of biomass inputs relative to fossil inputs in

the bio-based sectors. The lower the fossil prices the more ‘costly’ is the use of

biomass inputs. Without blending shares which are set as minimum blending

requirements for the bio-based sectors, less biomass would be used, i.e. additional

subsidies are required to maintain the blending shares at their minimum level.

Higher fossil energy prices increase the relative competitiveness of biomass inputs.

The sensitivity analysis shows that under the IntHighTech scenario with high fossil

energy prices the required subsidies become very low, see Table 2.

Table 2: Sensitivity analysis: Tax burden of using biomass inputs for liquid petrol production,

2030 in the Netherlands


Fuel consumption (mill. litres) 14286 13286 14786

Substitution share in % 60 62 60

Amount of biofuel (mill. litres) 8572 8237 8872

Subsidies, million € 293 121 521

€/litre of biofuel 0.034 0.015 0.059

The sensitivity analyses shows that the qualitative results are not fundamentally

different, but the extent of the effects can change substantially. The sensitivity

analysis shows that, despite the ambitious biomass blending targets in the

IntHighTech scenario, at crude oil prices of 112 USD/bbl, biomass becomes

competitive and increases the demand for bioenergy crops. The results, i.e. bio-

based production, however are still in line with the baseline situation. The required

subsidies for bio-based substitution of fossil energy carriers is sensitive to fossil

energy prices as displayed in Table 2.


summAryThe macro-economic analyses results cover impacts of the different bio-based

scenarios on the Dutch trade balance, GDP, sectoral effects (in particular

agriculture, energy and chemical), employment, all compared to the baseline

development where only a low share of biomass use (mainly for energy) is included.

To summarise the results of the quantitative analysis the following conclusions can

be drawn:

All bio-based scenarios have a positive effect on the trade balance of the –

Netherlands. In 2030 the net (positive) impact compared to the baseline

developments simulated by LEITAP are about 2000 (LowTech scenario) to 4000

(HighTech scenario) million € per year.

Imports of biomass (and biofuels, especially ethanol; depending on the scenario) –

are substantial, varying between over 2600 million € (NatLowTech) up to 7400

(IntHighTechAC) million € annually. South America, in particular, is a likely

major supplier.

The production of biomass used in the Dutch bio-based economy varies in value –

between some 180 Million € (IntLowTech) and almost 720 million €

(IntHighTechAC). This is substantial, but also reflects the relatively modest role

of national biomass resource production compared to imports.

In terms of employment generated, the share of employers working in the bio- –

based ‘part’ of the bio-based sectors (fuel, electricity and fine chemicals), the

total employment in these three sectors remains relatively stable over the

projected period, but the increasing share in employment in the ‘bio-based part’

indicates a growing importance of the bio-based economy for those sectors. The

results show that, with a shift towards a bio-based economy, agricultural

employment will continue to decline. However, a growing demand for biomass

will slightly dampen this structural change in agriculture.

The macro-economic modelling results confirm the large shares of 1st-generation –

biofuels for the LowTech scenarios as defined by the bottom-up approach. The

use of lignocellulosic biomass (both for fuels and for biomaterials) covers over

half the total demand for the HighTech scenarios in 2030.

This result, different from the bottom-up scenarios, where this share is even –

higher, is explained by the incorporation of continuous functions in the

modelling framework that basically take into account the lifetime of

investments and reasonable rates of change in production capacity over time.

With the base scenario assumptions, the share of lignocellulosic-based –

biomass applications will increase further after 2030 and overall costs will

go down. Furthermore, this share is sensitive to the rate of technological

progress (learning) of new technologies. A more conservative progress would

lead to lower shares and vice versa.

Required support levels to ensure the realisation of the projected shares of –

biofuel shares in the different scenario’s differ strongly between the scenarios

(the following data all relate to a reference oil price of 75 U$/bbl (in 2006 US$):

The NatLowTech scenario requires (for a modest share of 10%) a subsidy of –

about half a billion per year (and around 0.40 €/litre of biofuel).


For IntLowTech this is reduced to 350 million – € annually and 0.12 €/litre of

biofuel for a 20% share (especially due to lower costs of imports such as

ethanol). Costs increase again for the NatHighTech scenario (due to higher

feedstock costs).

IntHighTech achieves the 60% share of biofuels in 2030 with some 300 –

million € per year subsidies (and a low 0.034 €/litre biofuel subsidy). This

subsidy is only required for the 1st-generation biofuel part and, to some

extent, for 2nd-generation biodiesel; in this scenario competitive production

costs are achieved for 2nd-generation ethanol production, given the technology

assumptions and base oil price of 75U$/bbl.

In addition to the IntHighTech scenario, the IntHighTechAC scenario also –

includes bio-based production of natural gas and petroleum products and in

both the specialty and bulk chemical industries. Under the (extreme) high

blending shares assumed under IntHighTechAC imports of biomass are

projected to increase to more than 5 billion €, with most of these imports

coming from South American countries. Additional income and employment

under the IntHighTechAC scenario are mainly created in the petrol sector;

while around ¼ is generated in the electricity and chemical sectors.

These results are highly sensitive to the oil price; with lower oil prices, –

required support increases and vice versa. In addition, the scenarios assume

a fixed (and high) diesel demand in the transport sector. If this could be

replaced by 2nd-generation bioethanol or cheaper synfuels than Fischer-

Tropsch diesel (such as methanol or DME), costs would go down and be

competitive at the 75 U$/barrel reference oil price. However, this also implies

more adjustment investments in the transport sector (e.g. engine adjustments,

fuel distribution).

Shares of additional income across the bio-based industries in 2030 for the –

IntHighTech Scenario) due to biomass expansion amount to 19% for electricity

production using biomass, 78.5% for production of biofuels and 2.5% due to the

production of the assumed biomass-derived chemicals.


referencesBirur, D.K., Hertel, T.W. and Tyner, W.E. (2007), The Biofuel Boom: Implications for

World Food Markets. Paper presented at the Food Economy Conference, The Hague,

Netherlands.

Burniaux, J.-M. and T.P. Truong (2002), GTAP-E: An Energy-Environmental Version

of the GTAP Model. GTAP Technical Paper, No. 16. Revised Version.

CPB (2003), Four Futures of Europe, Netherlands Bureau for Economic Policy

Analysis, the Hague, the Netherlands. See: http://www.cpb.nl

F.O. Licht (2007), Licht Interactive Data.

Huang, H., F. van Tongeren, F. Dewbre and H. van Meijl (2004), A New

Representation of Agricultural Production Technology in GTAP. Paper presented at

the Seventh Annual Conference on Global Economic Analysis, June, Washington,

USA.

IEA – International Energy Agency (2008), IEA Bioenergy Task 39 ‘Commercializing

1st and 2nd Generation Liquid Biofuels from Biomass’. Various reports.

(http://www.task39.org/)

Kopetz, H., J.M. Jossart, H. Ragossnig, Ch. Metschina, (2007), European Biomass

Statistics 2007. A statistical report on the contribution of biomass to the energy

system in the EU 27. European Biomass Association (AEBIOM), Brussels

Meijl, H. van, T. van Rheenen, A. Tabeau and B. Eickhout (2005), The impact of

different policy environments on land use in Europe, Agriculture, Ecosystems and

Environment, 114-1: 21-38.

Wageningen UR and Netherlands Environmental Assessment Agency (2007),

Eururalis 2.0. A scenario study on Europe’s rural Areas to support policy

discussion.

Wielen, L. A. M. v. d., P. M. M. Nossin, et al. (2006). Potential of Coproduction of

Energy, Fuel and Chemicals from Biobased Renewable Resources. Transition Path:3

Co-production of Energy, Fuels and Chemicals. Delft, Delft University of technology.


AnneXTable A1. Regional aggregation

Regions Original GTAP v 6 regions

belu Belgium; Luxembourg

dnk Denmark

deu Germany

grc Greece

esp Spain

fra France

irl Ireland

ita Italy

nld Netherlands

aut Austria

prt Portugal

fin Finland

swe Sweden

gbr United Kingdom

euba Estonia; Latvia; Lithuania

euis Cyprus; Malta

cze Czech Republic

hun Hungary

pol Poland

svn Slovenia

svk Slovakia

apeu Bulgaria; Romania

reur Switzerland; Rest of EFTA; Rest of Europe; Albania; Croatia

fsu Russian Federation; Rest of Former Soviet Union

tur Turkey

meast Rest of Middle East

nafta United States, Canada, Mexico

ram Rest of North America; Colombia; Peru; Venezuela; Rest of Andean Pact; Argentina; Chile; Uruguay; Rest of South America; Central America; Rest of FTAA; Rest of the Caribbean

bra Brazil

oce Australia; New Zealand; Rest of Oceania

jp_ko Japan; Korea

chi China; Hong Kong; Taiwan; Rest of East Asia

ras Indonesia; Malaysia; Philippines; Singapore; Thailand; Vietnam; Rest of Southeast Asia; Bangladesh; India; Sri Lanka; Rest of South Asia; Canada

naf Morocco; Rest of North Africa

ssaf Botswana; Rest of South African CU; Malawi; Mozambique; Tanzania; Zambia; Zimbabwe; Rest of SADC; Madagascar; Uganda; Rest of Sub-Saharan Africa

saf South Africa


Table A2: Sector aggregation

Sectors in GTAP Original GTAP v 6 sectors

pdr Paddy and processed rice

wht Wheat

grain Cereal grains nec

oils Oil seeds

sug Sugar cane, sugar beet

hort Vegetables, fruit, nuts

wdcrp Woody crops (split out of ‘other crops’)

crops Plant-based fibres; Crops nec.

cattle Cattle, sheep, goats, horses; Wool, silk-worm cocoons; Meat: cattle, sheep, goats, horse

oap Animal products nec; Meat products nec.

milk Raw milk

dairy Dairy products

sugar Sugar

vol Vegetable oils and fats

ofd Food products nec.

agro Fishing; Beverages and tobacco products

frs Forestry

c_oil Oil

petro Petroleum, coal products

gas Gas; Gas manufacture, distribution

coa Coal

ely Electricity

fchem Fine chemicals (split out of ‘chemicals, plastic and rubber’)

bchem Bulk chemicals (rest of ‘chemicals, plastic and rubber’)

ind Minerals nec; Textiles; Wearing apparel; Leather products; Wood products; Paper products, publishing; Mineral products nec; Ferrous metals; Metals nec; Metal products; Motor vehicles and parts; Transport equipment nec; Electronic equipment; Machinery and equipment nec; Manufactures nec.

ser Water; Construction; Trade; Transport nec; Sea transport; Air transport; Communication; Financial services nec; Insurance; Business services nec; Recreation and other services; Public administration, defence, health and edu-cation; Dwellings


AnneX i: moDel Description: leitAp – gloBAl economy-wiDe projections

The analysis is carried out using an adapted version of the general equilibrium

model of the Global Trade Analysis Project [GTAP, Hertel, 1997]. The first part of

this section provides a brief overview of the standard GTAP model and the second

part focuses on extensions. The standard model was improved with a new land

allocation method that takes into account the degree of substitutability between

different types of land-use. A new land supply curve allowing for conversion and

abandonment of land is described in following section. The linkage of the adapted

economic model to the IMAGE framework in order to model yields and feed

efficiency rates is also described. Additionally, we used information from the

OECD’s Policy Evaluation Model (PEM) to improve the production structure and

introduced an endogenous quota mechanism. This chapter finishes with a

description of the projection methodology and a discussion of the database and the

regional as well as sectoral aggregation of the model for this study.

Global Trade Analyses Project: the standard model

GTAP was initiated with the aim of supporting high-level quantitative analysis of

international trade, resource and environmental issues in an economy-wide context.

The GTAP project is supported by the leading international agencies (e.g. WTO,

World Bank, OECD, and UNCTAD) in trade and development policy, as well as a

number of national agencies with active research programmes on these issues. The

GTAP project develops and maintains a database, a multi-region multi-sector

general equilibrium model. It also provides training courses and organises an

annual conference on global economic analysis. This project has grown rapidly

since its inception in 1993. There is no doubt that the GTAP database and its

associated modelling efforts represent a major achievement for advancing

quantitative analysis of international trade, resource and environmental issues.

The success of this approach is reflected in a high degree of academic recognition as

well as the increasing usage for policy analysis by international and national

agencies.

Standard model characteristics

There are basically two strands of quantitative modelling in policy analysis. One

approach is to build issue-specific models, depending on the question at hand.

These models will usually be capable of capturing many relevant aspects of one

specific policy question, but are of less use in a different policy context. The other

approach sets out to construct more general and flexible models, which do not

necessarily attempt to capture all details but are flexible enough to allow

elaborations in face of specific policy questions. The Global Trade Analysis Project

(GTAP) provides such a modelling framework.


The standard GTAP model15 is a comparative static multi-regional general

equilibrium model. In its standard version constant returns to scale and perfect

competition are assumed in all markets for outputs and inputs. A detailed

discussion of the basic algebraic model structure of the GTAP model can be found in

Hertel [1997]16. In the GTAP model each country or region is depicted within the

same structural model.

The general conceptual structure of a regional economy in the model is represented

in Figure I.1. Within each region, companies produce output, employing land,

labour, capital, and natural resources, and combine these with intermediate inputs.

This output is purchased by consumers, governments, the investment sector, and by

other companies. This output can also be sold for export. Land is only employed in

the agricultural sector, while capital and labour (both skilled and unskilled) are

mobile between all production sectors.

The model is characterised by an input-output structure (based on regional and

national input-output tables) that explicitly links industries in a value-added chain

from primary goods, through continuously higher stages of intermediate

processing, to the final assembling of goods and services for consumption. Inter-

sectoral linkages are direct, such as the input of steel in the production of transport

equipment, and indirect, via intermediate use in other sectors. The model captures

these linkages by modelling the companies’ use of factors and intermediate inputs.

The most important aspects of the model can be summarised as follows:

(i) it covers all world trade and production;

(ii) it includes intermediate linkages between sectors.

Figure I.1: The flow of production

31

The general conceptual structure of a regional economy in the model is represented in Figure I.1. Within each region, firms produce output, employing land, labour, capital, and natural resources, and combine these with intermediate inputs. Firm output is purchased by consumers, government, the investment sector, and by other firms. Firm output can also be sold for export. Land is only employed in the agricultural sector, while capital and labour (both skilled and unskilled) are mobile between all production sectors.

The model is characterised by an input-output structure (based on regional and national input-output tables) that explicitly links industries in a value-added chain from primary goods, through continuously higher stages of intermediate processing, to the final assembling of goods and services for consumption. Inter-sectoral linkages are direct, like the input of steel in the production of transport equipment, and indirect, via intermediate use in other sectors. The model captures these linkages by modelling firms’ use of factors and intermediate inputs. The most important aspects of the model can be summarized as follows:

(i) it covers all world trade and production; (ii) it includes intermediate linkages between sectors;

Figure I.1: The flow of production.

Output

Value

Added

Composite

Goods

ImportsCapital, Land,

Labor, and

Natural Resources

Exports Consumption

The consumer side is represented by the regional household to which the income of factors, tariff revenues and taxes are assigned. The regional household allocates its income to three expenditure categories: private household expenditures, government expenditures and savings. For the consumption of the private household, the non-homothetic Constant Difference of Elasticities (CDE) function is applied.

In the model, a representative producer for each sector of a country or region makes production decisions to maximise a profit function by choosing inputs of labour, capital, and intermediates to produce a single sectoral output. In the case of crop production, farmers also make decisions on land allocation. Intermediate inputs are produced domestically or imported, while primary factors cannot move across countries. Markets are typically assumed to be competitive. When making production decision, farmers and firms treat prices for output and input as given. The primary production factors land and capital are fully employed within each economy, and hence returns to land and capital are endogenously determined at the equilibrium, i.e. the aggregate supply of each factor equals its demand.

The production structure is depicted with a production tree with four nests (Figure I.2). The Leontief and the Constant Elasticity of Substitution (CES) functional forms are used to model the substitution relations between the inputs of the production process. In the output nest, the mix of factors and intermediate inputs are assembled together, forming the sectoral

15 Wedeliberatelyrefertothe‘standardGTAPmodel’asthemodelversionthatissupportedbythe

GTAPconsortium.GTAPusershavedevelopednumerousvariationsonthestandardmodel.Inthis

studywealsomakesomemodificationstothestandardmodel.Thesearediscussedmoreexten-

sivelyinsubsequentchapters.

16 Orontheinternethttp://www.agecon.purdue.edu/gtap/model/chap2.pdf


The consumer side is represented by the regional household to which the income of

factors, tariff revenues and taxes are assigned. The regional household allocates its

income to three expenditure categories: private household expenditures, government

expenditures and savings. For the consumption of the private household, the non-

homothetic Constant Difference of Elasticities (CDE) function is applied.

In the model, a representative producer for each sector of a country or region makes

production decisions to maximise a profit function by choosing inputs of labour,

capital, and intermediates to produce a single sectoral output. In the case of crop

production, farmers also make decisions on land allocation. Intermediate inputs are

produced domestically or imported, while primary factors cannot move across

countries. Markets are typically assumed to be competitive. When making

production decision, farmers and companies treat prices for output and input as

given. The primary production factors land and capital are fully employed within

each economy, and hence returns to land and capital are endogenously determined

at the equilibrium, i.e. the aggregate supply of each factor equals its demand.

The production structure is depicted with a production tree with four nests (Figure

I.2). The Leontief and the Constant Elasticity of Substitution (CES) functional forms

are used to model the substitution relations between the inputs of the production

process. In the output nest, the mix of factors and intermediate inputs are

assembled together, forming the sectoral output. The functional form can be

Leontief (fixed proportions) or CES. The substitution relations within the value

added nest are depicted by the CES function. While labour and capital are

considered mobile across sectors the Constant Elasticity of Transformation (CET)

function is used to represent the sluggish adjustment of the factor land; i.e. land can

only imperfectly move between alternative crop uses. The CES function is applied in

the composite intermediate nest depicting the substitution between domestic and

imported products. The last nest illustrates the relation between imports of the

same item from different regions. The Armington approach treats products from

different regions as imperfect substitutes.

Figure I.2: Production tree

32

output. The functional form can be Leontief (fixed proportions) or CES. The substitution relations within the value added nest are depicted by the CES function. While labour and capital are considered mobile across sectors the Constant Elasticity of Transformation (CET) function is used to represent the sluggish adjustment of the factor land. That is, land can only imperfectly move between alternative crop uses. The CES function is applied in the composite intermediate nest depicting the substitution between domestic and imported products. The last nest illustrates the relation between imports of the same good from different regions. The Armington approach treats products from different regions as imperfect substitutes. Figure I.2: Production tree

domestic foreign

Region 1 Region 2 Region r

Value added

Output sector i

CapitalLand SkLabUnskLab NatRes

Intermediate inputs

Leontief or CES

CESCES

CES

Source: Hertel (1997)

Prices on goods and factors adjust until all markets are simultaneously in (general) equilibrium. This means that we solve for equilibria in which all markets clear. While we model changes in gross trade flows, we do not model changes in net international capital flows. Rather our capital market closure involves fixed net capital inflows and outflows. (This does not preclude changes in gross capital flows). To summarise, factor markets are competitive, and labour and capital are mobile between sectors but not between regions.

The GTAP model includes two global institutions. All transport between regions is carried out by the international transport sector. The trading costs reflect the transaction costs involved in international trade, as well as the physical activity of transportation itself. In using transport inputs from all regions, the international transport sector minimises its costs under the Cobb-Douglas technology. The second global institution is the global bank, which takes the savings from all regions and purchases investment goods in all regions depending on the expected rates of return. The global bank guarantees that global savings are equal to global investments. With the standard closure, the model determines the trade balance in each region endogenously, and hence foreign capital inflows may supplement domestic savings. The model does not have an exchange rate variable. However, by choosing as a numerary an index of global factor prices, each region’s change of factor prices relative to the numerary directly reflects a change in the purchasing power of the region’s factor incomes on the world market. This can be directly interpreted as a change in the real exchange rate.

The welfare changes are measured by the equivalent variation, which can be computed from each region’s household expenditure function.

Taxes and other policy measures are included in the theory of the model at several levels. All policy instruments are represented as ad valorem tax equivalents. These create

Source: Hertel (1997)


Prices of goods and factors adjust until all markets are simultaneously in (general)

equilibrium. This means that we aim for equilibria in which all markets are clear.

While we model changes in gross trade flows, we do not model changes in net

international capital flows. Rather our capital market closure involves fixed net

capital inflows and outflows, though this does not preclude changes in gross capital

flows. To summarise, factor markets are competitive, and labour and capital are

mobile between sectors, but not between regions.

The GTAP model includes two global institutions. All transport between regions is

carried out by the international transport sector. The trading costs reflect the

transaction costs involved in international trade, as well as the physical activity of

transportation itself. In using transport inputs from all regions, the international

transport sector minimises its costs under the Cobb-Douglas technology. The

second global institution is the global bank, which takes the savings from all

regions and purchases investment goods in all regions depending on the expected

rates of return. The global bank guarantees that global savings are equal to global

investments. With the standard closure, the model determines the trade balance in

each region endogenously, and hence foreign capital inflows may supplement

domestic savings. The model does not have an exchange rate variable. However, by

choosing as a numerary an index of global factor prices, each region’s change of

factor prices relative to the numerary directly reflects a change in the purchasing

power of the region’s factor incomes on the world market. This can be directly

interpreted as a change in the real exchange rate.

The welfare changes are measured by the equivalent variation, which can be

computed from each region’s household expenditure function. Taxes and other

policy measures are included in the theory of the model at several levels. All policy

instruments are represented as ad valorem tax equivalents. These create wedges

between the undistorted prices and the policy-inclusive prices. Production taxes are

placed on intermediate or primary inputs, or on output. Trade policy instruments

include applied most-favoured nation tariffs, anti-dumping duties, countervailing

duties, price undertakings, export quotas, and other trade restrictions. Additional

internal taxes can be placed on domestic or imported intermediate inputs, and may

be applied at differential rates that discriminate against imports. Where relevant,

taxes are also placed on exports, and on primary factor income. Finally, where

relevant (as indicated by social accounting data) taxes are placed on final

consumption, and can be applied differentially to consumption of domestic and

imported goods.

The GTAP model is implemented in GEMPACK – a software package designed for

solving large applied general equilibrium models. A description of Gempack can be

found in Harrison and Pearson [2002]17.

17 Moreinformationcanbeobtainedatwww.monash.edu.au.policy/gempack.htm


Various GTAP users have developed adaptations of the standard model. Such

elaborations include increasing returns to scale and imperfect competition,

dynamic equilibrium formulations and incorporation of non-continuous policy

instruments such as the tariff rate quota that resulted from GATT Uruguay round,

or production quota as applied in the European milk and sugar sectors. For a model

version that uses both increasing returns and production quota, see Francois et al.

[2002] and Francois et al. [2003].

Extensions to the standard GTAP model

For the purpose of this study, we have applied a special purpose version of the GTAP

database and model, designed to make it more appropriate for the analyses of the

agricultural sector. We use information from the OECD’s Policy Evaluation Model

(PEM) to improve the production structure.

Figure I.3: Land allocation ‘tree’

33

wedges between the undistorted prices and the policy-inclusive prices. Production taxes are placed on intermediate or primary inputs, or on output. Trade policy instruments include applied most-favoured nation tariffs, antidumping duties, countervailing duties, price undertakings, export quotas, and other trade restrictions. Additional internal taxes can be placed on domestic or imported intermediate inputs, and may be applied at differential rates that discriminate against imports. Where relevant, taxes are also placed on exports, and on primary factor income. Finally, where relevant (as indicated by social accounting data) taxes are placed on final consumption, and can be applied differentially to consumption of domestic and imported goods.

The GTAP model is implemented in GEMPACK – a software package designed for solving large applied general equilibrium models. A description of Gempack can be found in Harrison and Pearson (2002)17.

Various GTAP users have developed adaptations of the standard model. Such elaborations include increasing returns to scale and imperfect competition, dynamic equilibrium formulations and incorporation of non-continuous policy instruments such as Tariff rate quota that resulted from GATT Uruguay round, or production quota as applied in the European milk and sugar sectors. For a model version that uses both increasing returns and production quota, see Francois et al. (2002) and Francois et al. (2003). Extensions to the standard GTAP model

For the purpose of this, we have applied a special purpose version of the GTAP database and model, designed to make it more appropriate for the analyses of the agricultural sector. We use information from the OECD’s Policy Evaluation Model (PEM) to improve the production structure. Figure I.3: Land allocation ‘tree’

L_

LiLj

Ln

CETσ1

Standard GTAP

land structure

L_

L HORT

L Pasture

L FCP

CET

σ1

L wheat

L Coarse

grains L oilseeds

CETσ3

Land structure based on PEM

L SUGL COP

σ2L OCRL NAG

17 More information can be obtained at www.monash.edu.au.policy/gempack.htm

Land allocation under the heterogeneity of land assumption:

The base version of GTAP represents land allocation in a CET structure (see left part

of Figure I.3). It is assumed that the various types of land-use are imperfectly

substitutable, but the substitutability is equal among all land-use types. We

extended the land-use allocation structure by taking into account that the degree of

substitutability of types of land differs between types [Huang et al., 2004]. We use

the OECD’s Policy Evaluation Model [OECD, 2003] structure, as it has more detail. It

distinguishes different types of land in a nested 3-level CET structure. The model


covers several types of land-use more or less suited to various crops (i.e. cereal

grains, oil-seeds, sugar cane/beet and other agricultural uses). The lower nest

assumes a constant elasticity of transformation between ‘vegetable fruit and nuts’

(HORT), ‘other crops’ (e.g. rice, plant-based fibres; OCR), the group of ‘Field Crops

and Pastures’ (FCP), and non-agricultural land (NAG)18. The transformation is

governed by the elasticity of transformation s1. The FCP group is itself a CET

aggregate of Cattle and Raw Milk (both Pasture), ‘Sugarcane and Beet’ (SUG), and

the group of ‘Cereal, Oil-seed and Protein crops’ (COP). Here the elasticity of

transformation is s2. Finally, the transformation of land within the upper nest, the

COP-group, is modelled with an elasticity s3.

In this way the degree of substitutability of types of land can be varied between the

nests. It captures to some extent agronomic features. In general it is assumed that

s3> s2 >s1. This means that it is easier to change the allocation of land within the

COP group, while it is more difficult to move land out of COP production into e.g.

vegetables. The values of the elasticities are taken from PEM (OECD, 2003).

Variability of total area

In the standard GTAP model, the total land supply is exogenous. In this version of

the model the total agricultural land supply is modelled using a land supply curve,

which specifies the relation between land supply and a rental rate [Meijl et al.,

2006]. Land supply to agriculture as a whole can be adjusted as a result of idling of

agricultural land, conversion of non-agricultural land to agriculture, conversion of

agricultural land to urban use and agricultural land abandonment.

The general idea is that when there is enough agricultural land available, increases

in demand for agricultural purposes will lead to land conversion to agricultural

land and a modest increase in rental rates (see left part of Figure I.4). However, if

almost all agricultural land is in use, then increases in demand will lead to

increases in rental rates (land becomes scarce, see right part of Figure I.4). When

land conversion and abandonment possibilities are low, the elasticity of land supply

in respect to land rental rates are low and land supply curve is steep.

18 Thenon-agriculturalcommoditiesdonotuselandinthecurrentGTAPmodelversion.However,

sincelandallocationinGTAPisdefinedoverallcommoditiesweaddthenon-agriculturallandto

thelandallocationtree.


Figure I.4: Land supply curve: land conversion and abandonment.

35

Figure I.4: Land supply curve: land conversion and abandonment.

We have assumed the following land supply function:

Land supply = a – b/real land price (1) where: a (>) is an asymptote, b is a positive parameter and the land supply elasticity E in respect of the land price is equal to

E = b/(a · real land price – b) (2)

We have calibrated the parameters a and b of the land supply function in such a way that it reproduces the GTAP land data for 2001. We have assumed the available agricultural land expressed by asymptote a is a sum of the agricultural land used currently is the production process and abounded agricultural land. We have used the agricultural land changes per region for 2030 predicted by FAO as indicators of agricultural land availability. In general, we have assumed that higher predicted increase of the agricultural land means higher availability of abounded agricultural land in the region. If the decrease of the agricultural land was predicted, we have assumed the scarcity of the agricultural land. Based on these consideration, we set the asymptote a. Having asymptote a, we have used GTAP land use data for 2001 as the land supply and observation for 2001 the initial GTAP real land prices equal to one to calculate the parameter b of the land supply function from the formula: b = a – Land supply (3)

and the land supply elasticity E in respect of the land price from formula (2).

Yield and feed conversion: linkage with IMAGE

Yields are only dealt with implicitly and that the feed livestock linkage in the GTAP is calculated using input-output coefficients. To improve the treatment of these issues the adjusted GTAP model was linked with the IMAGE model (Alcamo et al., 1998; IMAGE Team, 2001). The objective of IMAGE 2.2 is to explore the long-term dynamics of global environmental change. Ecosystem, crop and land-use models are used to compute land use on the basis of regional production of food, animal products and timber, and local climatic and

We have assumed the following land supply function:

Land supply = a – b/real land price (1)

where: a (>) is an asymptote, b is a positive parameter and the land supply elasticity

E in respect of the land price is equal to

E = b/(a · real land price – b) (2)

We have calibrated the parameters a and b of the land supply function in such a

way that it reproduces the GTAP land data for 2001. We have assumed the available

agricultural land expressed by asymptote a is a sum of the agricultural land used

currently is the production process and abounded agricultural land. We have used

the agricultural land changes per region for 2030 predicted by FAO as indicators of

agricultural land availability. In general, we have assumed that higher predicted

increase of the agricultural land means higher availability of abounded

agricultural land in the region. If the decrease of the agricultural land was

predicted, we have assumed the scarcity of the agricultural land. Based on these

consideration, we set the asymptote a.

Having asymptote a, we have used GTAP land-use data for 2001 as the land supply

and observation for 2001 the initial GTAP real land prices equal to one to calculate

the parameter b of the land supply function from the formula:

b = a – Land supply (3)

and the land supply elasticity E in respect of the land price from formula (2).


Yield and feed conversion: linkage with IMAGE

Yields are only dealt with implicitly and the feed livestock linkage in the GTAP is

calculated using input-output coefficients. To improve the treatment of these issues

the adjusted GTAP model was linked with the IMAGE model [Alcamo et al., 1998;

IMAGE Team, 2001]. The objective of IMAGE 2.2 is to explore the long-term

dynamics of global environmental change. Ecosystem, crop and land-use models are

used to compute land-use on the basis of regional production of food, animal products

and timber, and local climatic and terrain properties. The production of food and

animal products come from the adjusted GTAP model. The coinciding land-use change

and greenhouse gas emissions are determined. The atmospheric and ocean models

calculate changes in atmospheric composition by employing the emissions and by

taking oceanic CO2 uptake and atmospheric chemistry into consideration.

Subsequently, changes in climatic properties are computed by resolving oceanic heat

transport and the changes in radiative forcing by greenhouse gases and aerosols. The

impact models involve specific models for sea-level rise and land degradation risk

and make use of specific features of the ecosystem and crop models to depict

impacts on vegetation and crop growth [Leemans and Eickhout, 2004]. Since the

IMAGE model performs its calculations on a grid scale (of 0.5 by 0.5 degrees) the

heterogeneity of the land is taken into consideration [Leemans et al., 2002].

Yields

In the adjusted GTAP model, yield is only dependent on a trend factor and on prices.

The production structure used in this model implies that there are substitution

possibilities among factors. If land gets more expensive, the producer uses less land

and more other production factors such as capital. The impact is that land

productivity or yields will increase. Consequently, yield is dependent on an

exogenous part (the ‘trend’ component) and on an endogenous part with relative

factor prices (the ‘management factor’ component).

First, the exogenous trend of the yield is taken from the FAO study ‘Agriculture

towards 2030’ [FAO, 2003], in which the authors combined macro-economic

prospects with local expert knowledge. This approach led to best-guesses of the

technological change for each country for the coming 30 years. Given the scientific

status of the FAO work this data is used as exogenous input for a first model run

with the adjusted GTAP model. However, many studies indicated that this change in

productivity would be enhanced or reduced by other external factors, of which

climate change is mentioned most often [Rosenzweig et al., 1995; Parry et al., 2004;

Fischer, 1996]. These studies indicated that increasing adverse global impacts

because of climate change would be encountered with temperature increases above

3-4 °C compared to pre-industrial levels. These productivity changes need to be

included in a global study. Moreover, the amount of land expansion or land

abandonment will have an additional impact on productivity changes, since land

productivity is not homogenously distributed over each region.


In our approach, the exogenous part of the yield is updated in an iterative process

with the IMAGE model. The output of GTAP used for the IMAGE iteration is sectoral

production growth rates and a management factor describing the degree of land

intensification. Next, the IMAGE model calculates the yields, the demand for land

and the environmental consequences on crop growth productivity. IMAGE

simulates global land-use and land-cover changes by reconciling the land-use

demand with the land potential. The basic idea is to allocate gridded land cover

within different world regions until the total demands for this region are satisfied.

The results depend on changes in the demand for food and feed and a management

factor as computed by GTAP. Crop productivity is also affected by climate change.

The allocation of land-use types is done at grid cell level on the basis of specific

land allocation rules like crop productivity, distance to existing agricultural land,

distance to water bodies and a random factor [Alcamo et al., 1998]. This procedure

delivers additional changes in yields, which are given back to GTAP. A general

feature is that yields decline if large land expansions occur since marginal lands

are taken into production.

Segmentation of factor markets and endogenous production quota

If labour resources were perfectly mobile across domestic sectors, we would

observe equalised wages throughout the economy for workers with comparable

endowments. This is clearly not supported by evidence. Wage differentials between

agriculture and non-agriculture can be sustained in many countries (especially

developing countries) through limited off-farm labour migration [De Janvry, 1991].

Returns to assets invested in agriculture also tend to diverge from returns of

investment in other activities.

To capture these stylised facts, we incorporate segmented factor markets for labour

and capital by specifying a CET structure that transforms agricultural labour (and

capital) into non-agricultural labour (and capital) [Hertel and Keening, 2003]. This

specification has the advantage that it can be calibrated to available estimates of

agricultural labour supply response. In order to have separate market clearing

conditions for agriculture and non-agriculture, we need to segment these factor

markets, with a finite elasticity of transformation. We also have separate market

prices for each of these sets of endowments. The economy-wide endowment of

labour (and capital) remains fixed, so that any increase in supply of labour (capital)

to manufacturing labour (capital) has to be withdrawn from agriculture, and the

economy-wide resources constraint remains satisfied. The elasticities of

transformation can be calibrated to fit estimates of the elasticity of labour supply

from OECD [2001].

Agricultural production quotas

An output quota places a restriction on the volume of production. If such a supply

restriction is binding, it implies that consumers will pay a higher price than they

would pay in the case of an unrestricted interplay of demand and supply. A wedge is


created between the prices that consumers pay and the marginal cost for the

producer. The difference between the consumer price and the marginal costs is

known as the tax equivalent of the quota rent.

In our model both the EU milk quota and the sugar quota are implemented at

national level. Technically, this is achieved by formulating the quota as a

complementarity problem. This formulation allows for endogenous regime switches

from a state when the output quota is binding to a state when the quota becomes

non-binding. In addition, changes in the value of the quota rent are endogenously

determined. If t denotes the tax equivalent of the quota rent, and r denotes the

difference between the output quota –q and output q, then the complementary

problem can be written as:

r = –q – q

and

either t > 0 and r = 0 the quota is binding

or t = 0 and r=≥ 0 the quota is not binding.

Projection methodology

The four analysed scenarios do not differ by macro-economic assumptions

regarding the GDP, population and employment growth and productivity

development in agricultural sector. Both approaches, the macro-economic and the

bottom-up approach, are built on the same assumptions concerning these key

economic parameters.

The economic consequences for the agricultural system, on the basis of the scenario

assumptions outlined in the section above are calculated by GTAP. The output of

GTAP is, among others, sectoral production growth rates, land-use and a

management factor describing the degree of land intensification.

While key economic parameters are kept constant between the four scenarios,

technologies differ, e.g. the substitutability between different energy inputs and the

substitutability between inputs from different origin. Different values for these

parameters reflect the differences in technologies outlined in Part I of the report.


references for moDel DocumentAtion

Alcamo, J., Leemans, R., Kreileman, E. (1998), Global Change Scenarios of the 21st

Century. Results from the IMAGE 2.1 Model. Elsevier, London.

de Janvry, A. (1991), The Agrarian Question and Reformism in Latin America.

Baltimore: Johns Hopkins University Press.

Eickhout, B., van Meijl, H., Tabeau, A. and H. van Zeijts (2004), Between

Liberalization and Protection: Four Long-term Scenarios for Trade, Poverty and the

Environment, Presented at the Seventh Annual Conference on Global Economic

Analysis, June, Washington, USA.

Fischer, G., K. Frohberg, M.L. Parry, and C. Rosenzweig (1996), The potential effects

of climate change on world food production and security. In: Global Climate Change

and Agricultural Production [Bazzaz, F. and W. Sombroek (eds.)]. John Wiley and

Sons, Chichester, United Kingdom, 345 pp.

Food and Agriculture Organisation FAO (2003), Agriculture towards 2030. Rome.

Francois, J., van Meijl, H. and van Tongeren, F. (2002), Economic benefits of the

Doha round for The Netherlands (also world and EU level), Agricultural Economics

Research Institute, Report 6.03.02.

Francois, J., van Meijl, H. and van Tongeren, F. (2003), ‘Trade Liberalization and

Developing Countries Under the Doha Round’. CEPR Discussion Paper no. 4032.

London, Centre for Economic Policy Research. http://www.cepr.org/pubs/dps/

DP4032.asp.

Harrison, J. and Pearson, K. (2002), Getting started with GEMPACK: Hands-on

Examples. Monash University.

Hertel, T and Keening, R. (2003), “Assessing the Impact of WTO Reforms on World

Agricultural Markets: A New Approach”.

Hertel, T.W., 1997. Global Trade Analysis: Modelling and Applications. In:

Cambridge University Press, Cambridge.

Hertel, T.W., Anderson, K., Francois, J.F., Hoekman, B. and Martin, W. (1999),

Agriculture and non-agricultural liberalization in the millennium round, Paper

prepared for the World Bank’s integrated program of research and capacity building

to enhance participation of developing countries in the WTO 2000 Negotiations,

Presented at the Conference on Agriculture and the New Trade Agenda in the WTO

2000 Negotiations, Geneva, 1–2 October.


Huang, H., van Tongeren, F., Dewbre, J. and van Meijl, H. (2004), A New

Representation of Agricultural Production Technology in GTAP, Paper presented at

the Seventh Annual Conference on Global Economic Analysis, June, Washington,

USA.

IMAGE-team (2001), The IMAGE 2.2 implementation of the SRES scenarios. A

comprehensive analysis of emissions, climate change and impacts in the 21st

century. RIVM CD-ROM publication 481508018, National Institute for Public Health

and the Environment, Bilthoven, The Netherlands.

Leemans R, Eickhout B, Strengers B, Bouwman L, Schaeffer M (2002), The

consequences of uncertainties in land use, climate and vegetation responses on the

terrestrial carbon Science in China (Series C) Vol. (45), pp. 126-141

Leemans, R., Eickhout, B. (2004), Another reason for concern: regional and global

impacts on ecosystems for different levels of climate change. Global Environmental

Change Part A 14, 219-228.

Meijl, H. van, T. van Rheenen, A. Tabeau and B. Eickhout (2006), The impact of

different policy environments on land use in Europe, Agriculture, Ecosystems and

Environment, Vol. 114, pp. 21-38.

Netherlands Bureau for Economic Policy Analysis CPB, (2003), Sectoral TFP

Developments in the OECD. CPB Memorandum No. 58.To be outlined

OECD (2001), Market Effect of Crop Support Measures. Paris.

OECD (2003), OECD Agricultural Outlook 2003 – 2008. Paris.

OECD, 2003, Agricultural Policies in OECD Countries 2000. Monitoring and

Evaluation. Organization for Economic Co-operation and Development, Paris.

Parry, M.L., Rosenzweig, C., Iglesias, A., Livermore, M., Fischer, G.(2004), Effects of

climate change on global food production under SRES emissions and socio-economic

scenarios. Global Environmental Change 14, 53-67.

Rosenzweig, C., M.L. Parry, and G. Fischer (1995), World food supply. In: As Climate

Changes: International Impacts and Implications [Strzepek, K.M. and J.B. Smith

(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY,

USA, pp. 27-56.

Documents

AppenDiX ii mAcro-economic scenArios - WUR